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充血性心力衰竭

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Clinical epidemiology of heart failure with preserved ejection fraction (HFpEF) in comparatively young hospitalized patients Heart Failure With Preserved Ejection Fraction in the Young Effects of Dapagliflozin on Symptoms, Function and Quality of Life in Patients with Heart Failure and Reduced Ejection Fraction: Results from the DAPA-HF Trial Impact of Myocardial Scar on Prognostic Implication of Secondary Mitral Regurgitation in Heart Failure Nuclear Imaging of the Cardiac Sympathetic Nervous System: A Disease-Specific Interpretation in Heart Failure Evaluation and Management of Right-Sided Heart Failure: A Scientific Statement From the American Heart Association Frequency, predictors, and prognosis of ejection fraction improvement in heart failure: an echocardiogram-based registry study The Future of Biomarker-Guided Therapy for Heart Failure After the Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure (GUIDE-IT) Study Fluid Volume Overload and Congestion in Heart Failure: Time to Reconsider Pathophysiology and How Volume Is Assessed Economic and Quality-of-Life Outcomes of Natriuretic Peptide–Guided Therapy for Heart Failure
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Review Article2020 Jul 16;229:1-17.

JOURNAL：Am Heart J . Article Link

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

CR Olsen, RJ Mentz, KJ Anstrom et al. Keywords: machine learning; artificial intelligence;

ABSTRACT

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.

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